class_docstr: A 3x3 convolution layer with some utility functions.
class_docstr_tok: ["A","3x3","convolution","layer","with","some","utility","functions","."]
code: def __init__(self, inC, outC, last=False):
      # The properties must be named as "W" and "b".
      # This is in an attempt to try and be roughly compatible with https://github.com/FHPythonUtils/Waifu2x
      #  though this cannot necessarily account for transposition and other such things.
  
      # Massively overstate the weights to get them to be focused on,
      #  since otherwise the biases overrule everything
      self.W = Tensor.uniform(outC, inC, 3, 3) * 16.0
      # Layout-wise, blatant cheat, but serious_mnist does it. I'd guess channels either have to have a size of 1 or whatever the target is?
      # Values-wise, entirely different blatant cheat.
      # In most cases, use uniform bias, but tiny.
      # For the last layer, use just 0.5, constant.
      if last:
          self.b = Tensor.zeros(1, outC, 1, 1) + 0.5
      else:
          self.b = Tensor.uniform(1, outC, 1, 1)
  
file_path: tinygrad/examples/vgg7_helpers/waifu2x.py
imports: import numpy
  from tinygrad.tensor import Tensor
  from PIL import Image
  from tinygrad.helpers import fetch
label_desc: Use image node_id 1 to create a new Conv3x3Biased object  with example: obj = Conv3x3Biased(inC, outC, last)